Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
Sensor Data Fusion Using DSm Theory for Activity Recognition under Uncertainty in Home-Based Care
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
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This paper explores a revised evidential lattice structure designed for the purposes of activity recognition within Smart Homes. The proposed structure consists of three layers, an object layer, a context layer and an activity layer. These layers can be used to combine the mass functions derived from sensors along with sensor context and can subsequently be used to infer activities. We present the details of configuring the activity recognition process and perform an analysis on the relationship between the number of sensors and the number of layers. We also present the details of an empirical study on two public data sets. The results from this work has demonstrated that the proposed method is capable of correctly detecting activities with a high degree of accuracy (84.27%) with a dataset from MIT [4] and 82.49% with a dataset from the University of Amsterdam[10].